A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Tu, Keling; Wen, Shaozhe; Cheng, Ying; Xu, Yanan; Tong, Pan; Hou, Haonan; Gu, Rong; Wang, Jianhua; Wang, Fengge; Sun, Qun

doi:10.1186/s13007-022-00918-7

Cited by 21 publications

(10 citation statements)

References 63 publications

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“…In this study, four machine learning algorithms were used to test the model with the collected hyperspectral data sets, and the most suitable model was selected by comparing the results. The models included decision tree, 15 Gaussian Naive Bayes (GaussianNB), 16 perceptron, 17 and stochastic gradient descent. 18…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Liu,

Lai,

et al. 2024

Appl Spectrosc

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Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter “Gaia” hyperspectral sorter was used to extract 254 bands of plasma images, ranging from 900 nm to 1700 nm. Four different machine learning algorithms were used, including decision tree, Gaussian Naive Bayes (GaussianNB), perceptron, and stochastic gradient descent models. First, the preliminary classification accuracies were compared with the original data, which showed that the effects of the decision tree and GaussianNB models were better; their average accuracies could reach over 90%. Then, the feature dimension reduction was performed on the original data. The results showed that the effects of the decision tree were better with a classification accuracy of 93.33%. the classification of chylous plasma using different chylous indices suggested that the accuracies of the decision trees model both before and after the feature dimension reductions were the best with over 80% accuracy. The results of feature dimension reduction showed that the characteristic bands corresponded to all kinds of plasma, thereby showing their classification and identification potential. By applying the spectral characteristics of plasma to medical technology, this study suggested a rapid and effective method for the identification of chylous plasma and provided a reference for the blood detection technology to achieve the goal of reducing wasting blood resources and improving the work efficiency of the medical staff.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Liu,

Lai,

et al. 2024

Appl Spectrosc

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“…Machine learning models are capable of classifying the different groups in hyperspectral images and have a low computational complexity [14][15][16]. However, the spectral signs of similar crop varieties, the issue in processing high dimensional input features, the salt and pepper noise present in the image, all diminish the accuracy of crop classification [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

BOISO: Weight optimized U-Net architecture for segmentation of hyperspectral image

Bhuvaneshwarri,

Stateczny,

Kokku

et al. 2024

Preprint

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Recently, the Hyper Spectral Image (HSI) classification relies as a well-established study area in the topic related to Remote Sensing (RS). The classification of HSI is used in various applications such as military, agriculture, mineral mapping and so on. However, the existing techniques have underlying difficulties related to curse of dimensionalities and the lack of training data. To overcome these issues, this research using Butterfly Optimization Integrated Snake Optimization (BOISO) optimized U-Net for segmenting HSI. After data acquisition from Indian Pines dataset, the pre-processing is done using Weiner filter. Next to this, the proposed BOISO optimized U-Net is used to segment the pre-processed HSI. The spatial feature weight map is obtained based on spatial information path and corresponding features are obtained by multiplying semantic feature map. Finally, the feature map is linked with the spatial location to obtain final feature map which is optimized using the proposed BOISO. Then, the classification using hybrid classification approach is based on geometric mean of improved Deep Belief network (DBN) and Quantum Neural Network (QNN). The results exhibit that the BOISO achieves a sensitivity of 0.939 which is higher than SO, BOA, BES and PRO, with respective sensitivities of 0.877, 0.893, 0.847 and 0.857.

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“…As it is a staple crop, maize (Zea mays), ML for image analysis has been used in a variety of studies where image acquisition methods are standardized (17)(18)(19)(20)(21). The disease addressed in this work is Fusarium ear rot (FER) caused by the pathogen Fusarium verticillioides, and the investigation focuses on the area of infected tissue on the ear of the maize plant.…”

Section: Introductionmentioning

confidence: 99%

The Ear Unwrapper: A Maize Ear Image Acquisition and Machine Learning Pipeline for Disease Severity Phenotyping

Hudson

Brahmstedt³

et al. 2023

Preprint

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Background To successfully breed any plant for disease resistance, an accurate method of phenotyping disease severity is crucial. Fusarium ear rot (FER) is a common disease of maize (Zea mays) caused by the pathogen Fusarium verticillioides (Sacc.) Nirenberg (synonym F. moniliforme Sheldon, teleomorph Gibberella moniliformis Wineland). Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of area damaged, a system of semi-automated image acquisition and subsequent image analysis was designed. Results The tool created for image acquisition, “The Ear Unwrapper”, successfully obtains images of the full exterior of maize ears in roughly 10 seconds. To “unwrap” each ear of maize, the approach was to rotate the ear around its axis (the cob) and using a camera, take a continuous set of images of a single row of pixels and merge them to form one image. A set of images produced from The Ear Unwrapper were used to produce a probabilistic pixel classification model for predicting disease severity from unannotated images. The system was deliberately constructed using open-source software and off-the-shelf parts so that the image acquisition and analysis pipeline is adaptable for quantifying other maize ear pathogens, morphologies, and phenotypes. The data obtained from The Ear Unwrapper was correlated with two other phenotyping methods for validation and comparison and showed that the output from the system was reasonably accurate to determine lesion size. Conclusions This study provides an example of how a simplified image acquisition machine can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. Here, The Ear Unwrapper was built to image ears of maize, but other cylindrical objects can also be “unwrapped” to obtain a single image of the object’s exterior. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes, here, disease severity of Fusarium Ear Rot.

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A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Cited by 21 publications

References 63 publications

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques

BOISO: Weight optimized U-Net architecture for segmentation of hyperspectral image

The Ear Unwrapper: A Maize Ear Image Acquisition and Machine Learning Pipeline for Disease Severity Phenotyping

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